Overview

Dataset statistics

Number of variables15
Number of observations1296675
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory615.7 MiB
Average record size in memory497.9 B

Variable types

DateTime2
Text2
Categorical3
Numeric8

Alerts

zip is highly overall correlated with long and 1 other fieldsHigh correlation
lat is highly overall correlated with merch_latHigh correlation
long is highly overall correlated with zip and 1 other fieldsHigh correlation
merch_lat is highly overall correlated with latHigh correlation
merch_long is highly overall correlated with zip and 1 other fieldsHigh correlation
is_fraud is highly imbalanced (94.9%)Imbalance
amt is highly skewed (γ1 = 42.27787379)Skewed

Reproduction

Analysis started2023-10-17 02:37:11.469565
Analysis finished2023-10-17 02:38:54.856028
Duration1 minute and 43.39 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

Distinct1274791
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
Minimum2019-01-01 00:00:18
Maximum2020-06-21 12:13:37
2023-10-17T02:38:55.030786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:55.344571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct693
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.1 MiB
2023-10-17T02:38:55.843893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length43
Median length36
Mean length23.132597
Min length13

Characters and Unicode

Total characters29995460
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfraud_Rippin, Kub and Mann
2nd rowfraud_Heller, Gutmann and Zieme
3rd rowfraud_Lind-Buckridge
4th rowfraud_Kutch, Hermiston and Farrell
5th rowfraud_Keeling-Crist
ValueCountFrequency (%)
and 474111
 
15.7%
llc 97780
 
3.2%
inc 91939
 
3.0%
sons 73145
 
2.4%
ltd 70853
 
2.3%
plc 66475
 
2.2%
group 50447
 
1.7%
fraud_kutch 10560
 
0.3%
fraud_schaefer 9394
 
0.3%
fraud_streich 9250
 
0.3%
Other values (804) 2069403
68.4%
2023-10-17T02:38:56.955564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2910697
 
9.7%
r 2695758
 
9.0%
d 2139780
 
7.1%
e 1865710
 
6.2%
u 1857912
 
6.2%
n 1768848
 
5.9%
1726682
 
5.8%
f 1397378
 
4.7%
_ 1296675
 
4.3%
o 1129340
 
3.8%
Other values (45) 11206680
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22698472
75.7%
Uppercase Letter 3398527
 
11.3%
Space Separator 1726682
 
5.8%
Connector Punctuation 1296675
 
4.3%
Dash Punctuation 445070
 
1.5%
Other Punctuation 430034
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2910697
12.8%
r 2695758
11.9%
d 2139780
9.4%
e 1865710
 
8.2%
u 1857912
 
8.2%
n 1768848
 
7.8%
f 1397378
 
6.2%
o 1129340
 
5.0%
i 1080395
 
4.8%
t 873637
 
3.8%
Other values (15) 4979017
21.9%
Uppercase Letter
ValueCountFrequency (%)
L 477174
14.0%
C 312176
 
9.2%
S 301639
 
8.9%
B 278515
 
8.2%
H 260640
 
7.7%
K 216627
 
6.4%
G 192442
 
5.7%
R 181447
 
5.3%
M 179139
 
5.3%
P 159738
 
4.7%
Other values (15) 838990
24.7%
Other Punctuation
ValueCountFrequency (%)
, 400966
93.2%
' 29068
 
6.8%
Space Separator
ValueCountFrequency (%)
1726682
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1296675
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 445070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26096999
87.0%
Common 3898461
 
13.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2910697
 
11.2%
r 2695758
 
10.3%
d 2139780
 
8.2%
e 1865710
 
7.1%
u 1857912
 
7.1%
n 1768848
 
6.8%
f 1397378
 
5.4%
o 1129340
 
4.3%
i 1080395
 
4.1%
t 873637
 
3.3%
Other values (40) 8377544
32.1%
Common
ValueCountFrequency (%)
1726682
44.3%
_ 1296675
33.3%
- 445070
 
11.4%
, 400966
 
10.3%
' 29068
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29995460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2910697
 
9.7%
r 2695758
 
9.0%
d 2139780
 
7.1%
e 1865710
 
6.2%
u 1857912
 
6.2%
n 1768848
 
5.9%
1726682
 
5.8%
f 1397378
 
4.7%
_ 1296675
 
4.3%
o 1129340
 
3.8%
Other values (45) 11206680
37.4%

category
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size83.5 MiB
gas_transport
131659 
grocery_pos
123638 
home
123115 
shopping_pos
116672 
kids_pets
113035 
Other values (9)
688556 

Length

Max length14
Median length12
Mean length10.526079
Min length4

Characters and Unicode

Total characters13648903
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmisc_net
2nd rowgrocery_pos
3rd rowentertainment
4th rowgas_transport
5th rowmisc_pos

Common Values

ValueCountFrequency (%)
gas_transport 131659
10.2%
grocery_pos 123638
9.5%
home 123115
9.5%
shopping_pos 116672
9.0%
kids_pets 113035
8.7%
shopping_net 97543
7.5%
entertainment 94014
7.3%
food_dining 91461
 
7.1%
personal_care 90758
 
7.0%
health_fitness 85879
 
6.6%
Other values (4) 228901
17.7%

Length

2023-10-17T02:38:57.416269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gas_transport 131659
10.2%
grocery_pos 123638
9.5%
home 123115
9.5%
shopping_pos 116672
9.0%
kids_pets 113035
8.7%
shopping_net 97543
7.5%
entertainment 94014
7.3%
food_dining 91461
 
7.1%
personal_care 90758
 
7.0%
health_fitness 85879
 
6.6%
Other values (4) 228901
17.7%

Most occurring characters

ValueCountFrequency (%)
s 1429026
10.5%
e 1287345
9.4%
o 1231724
9.0%
n 1193757
8.7%
p 1083847
 
7.9%
t 1076942
 
7.9%
_ 1039039
 
7.6%
r 917535
 
6.7%
i 833007
 
6.1%
a 665234
 
4.9%
Other values (10) 2891447
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12609864
92.4%
Connector Punctuation 1039039
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1429026
11.3%
e 1287345
10.2%
o 1231724
9.8%
n 1193757
9.5%
p 1083847
8.6%
t 1076942
8.5%
r 917535
7.3%
i 833007
 
6.6%
a 665234
 
5.3%
g 606425
 
4.8%
Other values (9) 2285022
18.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1039039
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12609864
92.4%
Common 1039039
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 1429026
11.3%
e 1287345
10.2%
o 1231724
9.8%
n 1193757
9.5%
p 1083847
8.6%
t 1076942
8.5%
r 917535
7.3%
i 833007
 
6.6%
a 665234
 
5.3%
g 606425
 
4.8%
Other values (9) 2285022
18.1%
Common
ValueCountFrequency (%)
_ 1039039
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13648903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 1429026
10.5%
e 1287345
9.4%
o 1231724
9.0%
n 1193757
8.7%
p 1083847
 
7.9%
t 1076942
 
7.9%
_ 1039039
 
7.6%
r 917535
 
6.7%
i 833007
 
6.1%
a 665234
 
4.9%
Other values (10) 2891447
21.2%

amt
Real number (ℝ)

SKEWED 

Distinct52928
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.351035
Minimum1
Maximum28948.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2023-10-17T02:38:57.871271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.44
Q19.65
median47.52
Q383.14
95-th percentile196.31
Maximum28948.9
Range28947.9
Interquartile range (IQR)73.49

Descriptive statistics

Standard deviation160.31604
Coefficient of variation (CV)2.2788014
Kurtosis4545.645
Mean70.351035
Median Absolute Deviation (MAD)37.5
Skewness42.277874
Sum91222429
Variance25701.232
MonotonicityNot monotonic
2023-10-17T02:38:58.389123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.14 542
 
< 0.1%
1.04 538
 
< 0.1%
1.25 535
 
< 0.1%
1.02 533
 
< 0.1%
1.01 523
 
< 0.1%
1.05 519
 
< 0.1%
1.2 516
 
< 0.1%
1.23 515
 
< 0.1%
1.08 512
 
< 0.1%
1.11 509
 
< 0.1%
Other values (52918) 1291433
99.6%
ValueCountFrequency (%)
1 222
< 0.1%
1.01 523
< 0.1%
1.02 533
< 0.1%
1.03 499
< 0.1%
1.04 538
< 0.1%
1.05 519
< 0.1%
1.06 471
< 0.1%
1.07 498
< 0.1%
1.08 512
< 0.1%
1.09 496
< 0.1%
ValueCountFrequency (%)
28948.9 1
< 0.1%
27390.12 1
< 0.1%
27119.77 1
< 0.1%
26544.12 1
< 0.1%
25086.94 1
< 0.1%
17897.24 1
< 0.1%
15305.95 1
< 0.1%
15047.03 1
< 0.1%
15034.18 1
< 0.1%
14849.74 1
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.7 MiB
F
709863 
M
586812 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Length

2023-10-17T02:38:58.880581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T02:38:59.339463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
f 709863
54.7%
m 586812
45.3%

Most occurring characters

ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1296675
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1296675
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1296675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

zip
Real number (ℝ)

HIGH CORRELATION 

Distinct970
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48800.671
Minimum1257
Maximum99783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2023-10-17T02:38:59.757627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1257
5-th percentile7208
Q126237
median48174
Q372042
95-th percentile94569
Maximum99783
Range98526
Interquartile range (IQR)45805

Descriptive statistics

Standard deviation26893.222
Coefficient of variation (CV)0.55108305
Kurtosis-1.0964493
Mean48800.671
Median Absolute Deviation (MAD)23068
Skewness0.079680758
Sum6.327861 × 1010
Variance7.2324542 × 108
MonotonicityNot monotonic
2023-10-17T02:39:00.221810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73754 3646
 
0.3%
34112 3613
 
0.3%
48088 3597
 
0.3%
82514 3527
 
0.3%
49628 3123
 
0.2%
15484 3123
 
0.2%
85173 3119
 
0.2%
29819 3117
 
0.2%
38761 3113
 
0.2%
5461 3112
 
0.2%
Other values (960) 1263585
97.4%
ValueCountFrequency (%)
1257 2023
0.2%
1330 1031
 
0.1%
1535 515
 
< 0.1%
1545 1024
 
0.1%
1612 519
 
< 0.1%
1843 2597
0.2%
1844 2058
0.2%
2180 519
 
< 0.1%
2630 2090
0.2%
2908 550
 
< 0.1%
ValueCountFrequency (%)
99783 1568
0.1%
99747 12
 
< 0.1%
99746 540
 
< 0.1%
99323 2572
0.2%
99160 3030
0.2%
99116 15
 
< 0.1%
99113 1047
 
0.1%
99033 2458
0.2%
98836 524
 
< 0.1%
98665 500
 
< 0.1%

lat
Real number (ℝ)

HIGH CORRELATION 

Distinct968
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.537622
Minimum20.0271
Maximum66.6933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2023-10-17T02:39:00.759588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20.0271
5-th percentile29.8826
Q134.6205
median39.3543
Q341.9404
95-th percentile45.8433
Maximum66.6933
Range46.6662
Interquartile range (IQR)7.3199

Descriptive statistics

Standard deviation5.0758084
Coefficient of variation (CV)0.13171047
Kurtosis0.81296795
Mean38.537622
Median Absolute Deviation (MAD)3.3597
Skewness-0.18602768
Sum49970771
Variance25.763831
MonotonicityNot monotonic
2023-10-17T02:39:01.060040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.385 3646
 
0.3%
26.1184 3613
 
0.3%
42.5164 3597
 
0.3%
43.0048 3527
 
0.3%
39.8936 3123
 
0.2%
44.5995 3123
 
0.2%
33.2887 3119
 
0.2%
34.0326 3117
 
0.2%
33.4783 3113
 
0.2%
44.3346 3112
 
0.2%
Other values (958) 1263585
97.4%
ValueCountFrequency (%)
20.0271 1527
0.1%
20.0827 1032
 
0.1%
24.6557 2584
0.2%
26.1184 3613
0.3%
26.3304 542
 
< 0.1%
26.3771 518
 
< 0.1%
26.4215 3038
0.2%
26.4722 2524
0.2%
26.529 1549
0.1%
26.6939 1027
 
0.1%
ValueCountFrequency (%)
66.6933 12
 
< 0.1%
65.6899 540
 
< 0.1%
64.7556 1568
0.1%
48.8878 3030
0.2%
48.8856 2066
0.2%
48.8328 1533
0.1%
48.6669 1047
 
0.1%
48.6031 2973
0.2%
48.4786 2038
0.2%
48.34 3088
0.2%

long
Real number (ℝ)

HIGH CORRELATION 

Distinct969
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.226335
Minimum-165.6723
Maximum-67.9503
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2023-10-17T02:39:01.383828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-165.6723
5-th percentile-119.0825
Q1-96.798
median-87.4769
Q3-80.158
95-th percentile-73.5112
Maximum-67.9503
Range97.722
Interquartile range (IQR)16.64

Descriptive statistics

Standard deviation13.759077
Coefficient of variation (CV)-0.15249513
Kurtosis1.8558923
Mean-90.226335
Median Absolute Deviation (MAD)8.1527
Skewness-1.1501077
Sum-1.1699423 × 108
Variance189.3122
MonotonicityNot monotonic
2023-10-17T02:39:01.664952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-98.0727 3646
 
0.3%
-81.7361 3613
 
0.3%
-82.9832 3597
 
0.3%
-108.8964 3527
 
0.3%
-79.7856 3123
 
0.2%
-86.2141 3123
 
0.2%
-111.0985 3119
 
0.2%
-82.2027 3117
 
0.2%
-90.5142 3113
 
0.2%
-73.098 3112
 
0.2%
Other values (959) 1263585
97.4%
ValueCountFrequency (%)
-165.6723 1568
0.1%
-156.292 540
 
< 0.1%
-155.488 1032
0.1%
-155.3697 1527
0.1%
-153.994 12
 
< 0.1%
-124.4409 1043
0.1%
-124.2174 1547
0.1%
-124.1587 1031
0.1%
-124.1437 1526
0.1%
-123.9743 2036
0.2%
ValueCountFrequency (%)
-67.9503 2080
0.2%
-68.5565 1014
 
0.1%
-69.2675 519
 
< 0.1%
-69.4828 2050
0.2%
-69.9576 537
 
< 0.1%
-69.9656 3107
0.2%
-70.1031 9
 
< 0.1%
-70.239 1036
 
0.1%
-70.3001 2090
0.2%
-70.3457 1527
0.1%

city_pop
Real number (ℝ)

Distinct879
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88824.441
Minimum23
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2023-10-17T02:39:01.949425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile139
Q1743
median2456
Q320328
95-th percentile525713
Maximum2906700
Range2906677
Interquartile range (IQR)19585

Descriptive statistics

Standard deviation301956.36
Coefficient of variation (CV)3.3994738
Kurtosis37.614519
Mean88824.441
Median Absolute Deviation (MAD)2198
Skewness5.5938531
Sum1.1517643 × 1011
Variance9.1177644 × 1010
MonotonicityNot monotonic
2023-10-17T02:39:02.281062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
606 5496
 
0.4%
1595797 5130
 
0.4%
1312922 5075
 
0.4%
1766 4574
 
0.4%
241 4533
 
0.3%
2906700 4168
 
0.3%
276002 4155
 
0.3%
302 4147
 
0.3%
910148 4073
 
0.3%
198 4067
 
0.3%
Other values (869) 1251257
96.5%
ValueCountFrequency (%)
23 2049
0.2%
37 1013
 
0.1%
43 2034
0.2%
46 3040
0.2%
47 511
 
< 0.1%
49 1054
 
0.1%
51 1016
 
0.1%
52 518
 
< 0.1%
53 2610
0.2%
60 1045
 
0.1%
ValueCountFrequency (%)
2906700 4168
0.3%
2504700 2033
 
0.2%
2383912 521
 
< 0.1%
1595797 5130
0.4%
1577385 2563
0.2%
1526206 3517
0.3%
1417793 8
 
< 0.1%
1382480 2056
0.2%
1312922 5075
0.4%
1263321 3629
0.3%

job
Text

Distinct494
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.5 MiB
2023-10-17T02:39:02.744992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length59
Median length38
Mean length20.227102
Min length3

Characters and Unicode

Total characters26227978
Distinct characters53
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPsychologist, counselling
2nd rowSpecial educational needs teacher
3rd rowNature conservation officer
4th rowPatent attorney
5th rowDance movement psychotherapist
ValueCountFrequency (%)
engineer 131756
 
4.6%
officer 110915
 
3.9%
manager 61124
 
2.1%
scientist 55878
 
1.9%
designer 52218
 
1.8%
surveyor 49062
 
1.7%
teacher 38126
 
1.3%
psychologist 32600
 
1.1%
research 29754
 
1.0%
editor 28725
 
1.0%
Other values (456) 2289024
79.5%
2023-10-17T02:39:03.583525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2803032
 
10.7%
i 2386346
 
9.1%
r 2198669
 
8.4%
a 1813638
 
6.9%
t 1782302
 
6.8%
n 1764769
 
6.7%
1582507
 
6.0%
o 1491775
 
5.7%
s 1444701
 
5.5%
c 1323152
 
5.0%
Other values (43) 7637087
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22784440
86.9%
Space Separator 1582507
 
6.0%
Uppercase Letter 1369269
 
5.2%
Other Punctuation 443484
 
1.7%
Close Punctuation 24139
 
0.1%
Open Punctuation 24139
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2803032
12.3%
i 2386346
10.5%
r 2198669
9.6%
a 1813638
 
8.0%
t 1782302
 
7.8%
n 1764769
 
7.7%
o 1491775
 
6.5%
s 1444701
 
6.3%
c 1323152
 
5.8%
l 999624
 
4.4%
Other values (16) 4776432
21.0%
Uppercase Letter
ValueCountFrequency (%)
C 156704
11.4%
E 145426
10.6%
P 143111
10.5%
S 137500
10.0%
T 113148
 
8.3%
M 89545
 
6.5%
A 88466
 
6.5%
F 68651
 
5.0%
D 58034
 
4.2%
R 55841
 
4.1%
Other values (11) 312843
22.8%
Other Punctuation
ValueCountFrequency (%)
, 312210
70.4%
/ 123567
 
27.9%
' 7707
 
1.7%
Space Separator
ValueCountFrequency (%)
1582507
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24139
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24153709
92.1%
Common 2074269
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2803032
11.6%
i 2386346
 
9.9%
r 2198669
 
9.1%
a 1813638
 
7.5%
t 1782302
 
7.4%
n 1764769
 
7.3%
o 1491775
 
6.2%
s 1444701
 
6.0%
c 1323152
 
5.5%
l 999624
 
4.1%
Other values (37) 6145701
25.4%
Common
ValueCountFrequency (%)
1582507
76.3%
, 312210
 
15.1%
/ 123567
 
6.0%
) 24139
 
1.2%
( 24139
 
1.2%
' 7707
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26227978
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2803032
 
10.7%
i 2386346
 
9.1%
r 2198669
 
8.4%
a 1813638
 
6.9%
t 1782302
 
6.8%
n 1764769
 
6.7%
1582507
 
6.0%
o 1491775
 
5.7%
s 1444701
 
5.5%
c 1323152
 
5.0%
Other values (43) 7637087
29.1%

dob
Date

Distinct968
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
Minimum1924-10-30 00:00:00
Maximum2005-01-29 00:00:00
2023-10-17T02:39:03.893370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:39:04.221500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

unix_time
Real number (ℝ)

Distinct1274823
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3492436 × 109
Minimum1.325376 × 109
Maximum1.3718168 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2023-10-17T02:39:04.510683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.325376 × 109
5-th percentile1.328672 × 109
Q11.3387507 × 109
median1.3492497 × 109
Q31.3593854 × 109
95-th percentile1.3698306 × 109
Maximum1.3718168 × 109
Range46440799
Interquartile range (IQR)20634633

Descriptive statistics

Standard deviation12841278
Coefficient of variation (CV)0.0095173904
Kurtosis-1.0875405
Mean1.3492436 × 109
Median Absolute Deviation (MAD)10358807
Skewness0.0033779498
Sum1.7495305 × 1015
Variance1.6489843 × 1014
MonotonicityIncreasing
2023-10-17T02:39:04.810577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1370177227 4
 
< 0.1%
1335110521 4
 
< 0.1%
1370050667 4
 
< 0.1%
1367602155 3
 
< 0.1%
1364686521 3
 
< 0.1%
1369587838 3
 
< 0.1%
1337306743 3
 
< 0.1%
1343668520 3
 
< 0.1%
1341944714 3
 
< 0.1%
1340650327 3
 
< 0.1%
Other values (1274813) 1296642
> 99.9%
ValueCountFrequency (%)
1325376018 1
< 0.1%
1325376044 1
< 0.1%
1325376051 1
< 0.1%
1325376076 1
< 0.1%
1325376186 1
< 0.1%
1325376248 1
< 0.1%
1325376282 1
< 0.1%
1325376308 1
< 0.1%
1325376318 1
< 0.1%
1325376361 1
< 0.1%
ValueCountFrequency (%)
1371816817 1
< 0.1%
1371816816 1
< 0.1%
1371816752 1
< 0.1%
1371816739 1
< 0.1%
1371816728 1
< 0.1%
1371816696 1
< 0.1%
1371816683 1
< 0.1%
1371816656 1
< 0.1%
1371816562 1
< 0.1%
1371816522 1
< 0.1%

merch_lat
Real number (ℝ)

HIGH CORRELATION 

Distinct1247805
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.537338
Minimum19.027785
Maximum67.510267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2023-10-17T02:39:05.607681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19.027785
5-th percentile29.751653
Q134.733572
median39.36568
Q341.957164
95-th percentile46.00353
Maximum67.510267
Range48.482482
Interquartile range (IQR)7.223592

Descriptive statistics

Standard deviation5.1097884
Coefficient of variation (CV)0.13259318
Kurtosis0.79599391
Mean38.537338
Median Absolute Deviation (MAD)3.397536
Skewness-0.18191543
Sum49970403
Variance26.109937
MonotonicityNot monotonic
2023-10-17T02:39:05.897156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.305966 4
 
< 0.1%
41.937796 4
 
< 0.1%
42.265012 4
 
< 0.1%
41.301611 4
 
< 0.1%
34.134994 4
 
< 0.1%
37.669788 4
 
< 0.1%
39.348185 4
 
< 0.1%
32.64469 4
 
< 0.1%
42.749184 4
 
< 0.1%
38.050673 4
 
< 0.1%
Other values (1247795) 1296635
> 99.9%
ValueCountFrequency (%)
19.027785 1
< 0.1%
19.027804 1
< 0.1%
19.029798 1
< 0.1%
19.031242 1
< 0.1%
19.032277 1
< 0.1%
19.033288 1
< 0.1%
19.034282 1
< 0.1%
19.034687 1
< 0.1%
19.035472 1
< 0.1%
19.036312 1
< 0.1%
ValueCountFrequency (%)
67.510267 1
< 0.1%
67.441518 1
< 0.1%
67.397018 1
< 0.1%
67.188111 1
< 0.1%
67.064277 1
< 0.1%
66.835174 1
< 0.1%
66.682905 1
< 0.1%
66.67355 1
< 0.1%
66.664673 1
< 0.1%
66.659242 1
< 0.1%

merch_long
Real number (ℝ)

HIGH CORRELATION 

Distinct1275745
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.226465
Minimum-166.67124
Maximum-66.950902
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2023-10-17T02:39:06.192467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-166.67124
5-th percentile-119.33009
Q1-96.897276
median-87.438392
Q3-80.236796
95-th percentile-73.354218
Maximum-66.950902
Range99.72034
Interquartile range (IQR)16.660479

Descriptive statistics

Standard deviation13.771091
Coefficient of variation (CV)-0.15262806
Kurtosis1.8484792
Mean-90.226465
Median Absolute Deviation (MAD)8.227889
Skewness-1.1469599
Sum-1.169944 × 108
Variance189.64294
MonotonicityNot monotonic
2023-10-17T02:39:06.495213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.116414 4
 
< 0.1%
-81.219189 4
 
< 0.1%
-74.618269 4
 
< 0.1%
-85.326323 3
 
< 0.1%
-84.890305 3
 
< 0.1%
-88.49309 3
 
< 0.1%
-84.100102 3
 
< 0.1%
-97.527227 3
 
< 0.1%
-85.3444 3
 
< 0.1%
-86.037494 3
 
< 0.1%
Other values (1275735) 1296642
> 99.9%
ValueCountFrequency (%)
-166.671242 1
< 0.1%
-166.670132 1
< 0.1%
-166.669638 1
< 0.1%
-166.666179 1
< 0.1%
-166.664828 1
< 0.1%
-166.662888 1
< 0.1%
-166.661968 1
< 0.1%
-166.659277 1
< 0.1%
-166.657834 1
< 0.1%
-166.657174 1
< 0.1%
ValueCountFrequency (%)
-66.950902 1
< 0.1%
-66.955996 1
< 0.1%
-66.95654 1
< 0.1%
-66.958659 1
< 0.1%
-66.958751 1
< 0.1%
-66.959178 1
< 0.1%
-66.961923 1
< 0.1%
-66.962913 1
< 0.1%
-66.963918 1
< 0.1%
-66.963975 1
< 0.1%

is_fraud
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.7 MiB
0
1289169 
1
 
7506

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Length

2023-10-17T02:39:06.749738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T02:39:06.992515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1296675
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1296675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1296675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Interactions

2023-10-17T02:38:40.846664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:15.244219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:18.360960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:21.575656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:26.950305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:31.112075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:34.234378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:37.501634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:41.483717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:15.644226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:18.759295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:21.971738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:27.530540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:31.503461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:34.654273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:37.896295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:42.070523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:16.030115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:19.156375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:22.410137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:28.097456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:31.895648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:35.112876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:38.315691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:42.627954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:16.433017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:19.566182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:22.859828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:28.636942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:32.290212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:35.545098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:38.689313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:43.246798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:16.805038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:19.959806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:23.349545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:29.167161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:32.676463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:35.932050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:39.072361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:43.881637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:17.197712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:20.390718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:24.298764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:29.733910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:33.083067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:36.325325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:39.480317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:44.460727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:17.580905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:20.782554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:25.841475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:30.315082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:33.481523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:36.713711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:39.874879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:45.029243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:17.954867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:21.168536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:26.405575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:30.713451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:33.849916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:37.097739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T02:38:40.282207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-17T02:39:07.181387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
amtziplatlongcity_popunix_timemerch_latmerch_longcategorygenderis_fraud
amt1.0000.0010.012-0.000-0.0240.0010.0120.0000.0200.0000.000
zip0.0011.000-0.162-0.959-0.0400.001-0.162-0.9570.0110.1190.005
lat0.012-0.1621.0000.106-0.2650.0010.9910.1050.0110.1010.008
long-0.000-0.9590.1061.0000.087-0.0010.1060.9980.0090.0910.006
city_pop-0.024-0.040-0.2650.0871.000-0.003-0.2640.0860.0140.0890.004
unix_time0.0010.0010.001-0.001-0.0031.0000.001-0.0010.0010.0000.018
merch_lat0.012-0.1620.9910.106-0.2640.0011.0000.1040.0110.1030.008
merch_long0.000-0.9570.1050.9980.086-0.0010.1041.0000.0090.0820.005
category0.0200.0110.0110.0090.0140.0010.0110.0091.0000.0540.071
gender0.0000.1190.1010.0910.0890.0000.1030.0820.0541.0000.008
is_fraud0.0000.0050.0080.0060.0040.0180.0080.0050.0710.0081.000

Missing values

2023-10-17T02:38:46.754699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-17T02:38:49.740363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

trans_date_trans_timemerchantcategoryamtgenderziplatlongcity_popjobdobunix_timemerch_latmerch_longis_fraud
02019-01-01 00:00:18fraud_Rippin, Kub and Mannmisc_net4.97F2865436.0788-81.17813495Psychologist, counselling1988-03-09132537601836.011293-82.0483150
12019-01-01 00:00:44fraud_Heller, Gutmann and Ziemegrocery_pos107.23F9916048.8878-118.2105149Special educational needs teacher1978-06-21132537604449.159047-118.1864620
22019-01-01 00:00:51fraud_Lind-Buckridgeentertainment220.11M8325242.1808-112.26204154Nature conservation officer1962-01-19132537605143.150704-112.1544810
32019-01-01 00:01:16fraud_Kutch, Hermiston and Farrellgas_transport45.00M5963246.2306-112.11381939Patent attorney1967-01-12132537607647.034331-112.5610710
42019-01-01 00:03:06fraud_Keeling-Cristmisc_pos41.96M2443338.4207-79.462999Dance movement psychotherapist1986-03-28132537618638.674999-78.6324590
52019-01-01 00:04:08fraud_Stroman, Hudson and Erdmangas_transport94.63F1891740.3750-75.20452158Transport planner1961-06-19132537624840.653382-76.1526670
62019-01-01 00:04:42fraud_Rowe-Vandervortgrocery_net44.54F6785137.9931-100.98932691Arboriculturist1993-08-16132537628237.162705-100.1533700
72019-01-01 00:05:08fraud_Corwin-Collinsgas_transport71.65M2282438.8432-78.60036018Designer, multimedia1947-08-21132537630838.948089-78.5402960
82019-01-01 00:05:18fraud_Herzog Ltdmisc_pos4.27F1566540.3359-79.66071472Public affairs consultant1941-03-07132537631840.351813-79.9581460
92019-01-01 00:06:01fraud_Schoen, Kuphal and Nitzschegrocery_pos198.39F3704036.5220-87.3490151785Pathologist1974-03-28132537636137.179198-87.4853810
trans_date_trans_timemerchantcategoryamtgenderziplatlongcity_popjobdobunix_timemerch_latmerch_longis_fraud
12966652020-06-21 12:08:42fraud_Gulgowski LLChome72.17M4977545.7549-84.447095Electrical engineer1994-02-09137181652244.938461-83.9962340
12966662020-06-21 12:09:22fraud_Hyatt, Russel and Gleichnerhealth_fitness7.30F6095841.0646-87.59172135Psychotherapist, child2004-05-08137181656240.556811-88.0923390
12966672020-06-21 12:10:56fraud_Hahn, Douglas and Schowaltertravel19.71M3384428.0758-81.592933804Exercise physiologist1991-01-01137181665627.465871-81.5118040
12966682020-06-21 12:11:23fraud_Metz, Russel and Metzkids_pets100.85F3907332.1530-90.121719685Fine artist1984-12-24137181668331.377697-90.5284500
12966692020-06-21 12:11:36fraud_Stiedemann Incmisc_pos37.38F6885941.4972-98.7858509Nurse, children's1980-09-15137181669641.728638-99.0396600
12966702020-06-21 12:12:08fraud_Reichel Incentertainment15.56M8473537.7175-112.4777258Geoscientist1961-11-24137181672836.841266-111.6907650
12966712020-06-21 12:12:19fraud_Abernathy and Sonsfood_dining51.70M2179039.2667-77.5101100Production assistant, television1979-12-11137181673938.906881-78.2465280
12966722020-06-21 12:12:32fraud_Stiedemann Ltdfood_dining105.93M8832532.9396-105.8189899Naval architect1967-08-30137181675233.619513-105.1305290
12966732020-06-21 12:13:36fraud_Reinger, Weissnat and Strosinfood_dining74.90M5775643.3526-102.54111126Volunteer coordinator1980-08-18137181681642.788940-103.2411600
12966742020-06-21 12:13:37fraud_Langosh, Wintheiser and Hyattfood_dining4.30M5987145.8433-113.8748218Therapist, horticultural1995-08-16137181681746.565983-114.1861100